Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Jafari, Reza; Bashari, Hossein; Tarkesh, Mostafa
• Is Part Of: Arid land research and management. Volume 31:Issue 1(2017)
• Page Start: 94
• Page End: 110
• Publication Date: 2017-01-02
• Subjects: Four-factor method -- rangeland condition -- remote-sensing data -- vegetation monitoring -- vegetation type; Arid soils -- Periodicals; Arid regions agriculture -- Periodicals; Desert reclamation -- Periodicals; 631.47154
• DOI: 10.1080/15324982.2016.1224955
• Languages: English
• ISSNs: 1532-4982

Abstract

ABSTRACT: Monitoring is essential for appropriate rangeland management. The present study aimed to examine the potential of moderate resolution imaging spectroradiometer (MODIS) satellite imagery in rangeland condition assessment and monitoring within and across vegetation types in the arid and semi-arid rangelands of central Iran. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were calculated from MODIS Aqua/Terra Level 1B data (related to 2003–2013). The obtained values were compared with vegetation cover measurements and rangeland condition classes at 110 sampling sites using linear regression, one-way analysis of variance (ANOVA), independent-samples t-tests, and Tukey's pairwise comparisons. The results showed that two indices made stronger predictions of vegetation cover within a vegetation type ( R 2 > 0.87, P < 0.001) than across vegetation types ( R 2 > 0.51, P < 0.001). Both NDVI and EVI worked well across vegetation types ( P ≤ 0.001) in predicting rangeland condition classes (poor, fair, and good), but their performance varied between vegetation types. The NDVI classified about 73, 19, and 7.5% of the rangelands in poor, fair, and good condition classes, respectively. The good performance of MODIS NDVI index at different landscapes indicates that this index has high potential in detecting vegetation cover and discriminating different condition classes, therefore, it can be used to aid field- based techniques in rangeland condition assessment and monitoring.